Currently, biologists are collecting enormous amounts of ‘omics’ data in a vast number of different databases. Predictive, data-driven computational models are needed to understand the complex, multi-scale biological networks underlying these high-throughput datasets. Such models are non-linear and contain many parameters, which are difficult (or impossible) to measure directly. Instead, parameters need to be inferred from data. This approach is called reverse-engineering. It has tremendous potential for several areas, such as biotechnology and systems biology, since it allows us to develop models with unprecedented accuracy and predictive power. This is achieved through an iterative refinement of our models compared to quantitative ‘omics’ data, a process called the systems-biology modelling cycle. Many methods have been developed that deal with specific steps in this cycle (data analysis, model building/discrimination, parameter estimation/identifiability analysis, uncertainty quantification, and optimal experimental design), but we still lack an over-arching, easy-to-use software framework that supports the modelling cycle in its entirety, allowing its widespread application. This project aims at improving accessibility of the data, and developing novel algorithms and tools implemented in such a general framework, which will enable the efficient transfer of cutting-edge modelling and optimisation methods from an academic research setting to private biotechnology partners. We will use representative biological and biotechnological applications as benchmark problems to develop robust and generally applicable methodology. The availability of such tools to the biotechnology sector (and other industries) will greatly enhance our ability to design and optimise complex production processes, especially those of nutraceuticals, biopharmaceuticals, or fine chemicals based on engineered organisms such as bacteria, yeast or plants.